qwen-image-3.0/text-to-image

text-to-image

Qwen Image 3.0 Text-to-Image API

qwen-image-3.0/text-to-image

Text-to-image

Generates images from a text prompt at resolutions up to 2048×2048, with automatic prompt rewriting and prompt-guided resolution selection, building on Qwen strength in complex text rendering and precise prompt adherence

Compare models

Qwen Image 3.0 Text-to-Image is developed by Alibaba. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.

INPUT

Prompt *

Negative Prompt

Size

Width

Height

1024 × 1024 pxRange: 256 - 4096

Number

Seed

Prompt extend

Prompt extend mode

OUTPUT

Idle

Your generated images will appear here

Configure your settings and click Run to get started

Your request will cost $0.04 per run. For $10 you can run this model approximately 250 times.

Here's what you can do next:

Image-to-Video Image-to-Image

Parameters

Code Example
import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "qwen-image-3.0/text-to-image",  # Required. model name
    "prompt": "A beautiful landscape with mountains and lake",  # Required. The text prompt describing the image to generate
    "size": "example_value",  # Output resolution as width*height (e
    "n": 1,  # Number of images to generate. (min: 1, max: 4)
    "negative_prompt": "example_value",  # Describes content that should NOT appear in the generated image
    "seed": 0,  # Seed for reproducible generation, range [0, 2147483647]. (min: 0, max: 2147483647)
    "prompt_extend": True,  # Enable intelligent prompt rewriting for richer results
    "prompt_extend_mode": "direct",  # Prompt rewriting strategy. options: direct | agent
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] == "completed":
            print("Generated image:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

image_url = check_status()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateImage

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "qwen-image-3.0/text-to-image",
    "prompt": "A beautiful landscape with mountains and lake"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

Response

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Check Status

Poll the prediction endpoint to check the current status of your request.

GET/api/v1/model/prediction/{prediction_id}

Polling Example

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Response

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

The following parameters are accepted in the request body.

Total: 8Required: 2Optional: 6

modelstringrequired

model name

Default: "qwen-image-3.0/text-to-image"

promptstringrequired

The text prompt describing the image to generate.

sizestring

Output resolution as width*height (e.g. 2048*2048). Omit to let the model pick a resolution automatically from the prompt. Valid range: 512*512–2048*2048.

ninteger

Number of images to generate.

Default: 1Min: 1Max: 4

negative_promptstring

Describes content that should NOT appear in the generated image.

seedinteger

Seed for reproducible generation, range [0, 2147483647]. Omit for a random seed.

Min: 0Max: 2147483647

prompt_extendboolean

Enable intelligent prompt rewriting for richer results.

Default: true

prompt_extend_modestring

Prompt rewriting strategy. 'direct' (DPE) rewrites in a single pass; 'agent' (APE) uses an agentic rewrite pipeline.

Default: "direct"

directagent

Example Request Body

{
  "model": "qwen-image-3.0/text-to-image",
  "prompt": "A beautiful landscape",
  "n": 1,
  "prompt_extend": true,
  "prompt_extend_mode": "direct"
}

Output Schema

The API returns a prediction response with the generated output URLs.

modelstring

model name

Default: "qwen-image-3.0/text-to-image"

created_atstring

ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

idstring

Unique identifier for the prediction.

outputsarray

Array of URLs to the generated images. Null when status is not completed. URLs are valid for about 24 hours — download promptly.

statusstring

Status of the task: created, processing, completed, or failed.

Example Response

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrates 400+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

export ATLASCLOUD_API_KEY="your-api-key-here"

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 400+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 400+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "paths": {
    "/api/v1/model/generateImage": {
      "post": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        },
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        },
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "schema": {
              "type": "string",
              "description": "Request ID"
            },
            "required": true
          }
        ]
      },
      "x-api-name": "model_result"
    }
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "qwen-image-3.0/text-to-image"
          },
          "prompt": {
            "description": "The text prompt describing the image to generate.",
            "type": "string",
            "minLength": 1
          },
          "size": {
            "description": "Output resolution as width*height (e.g. 2048*2048). Omit to let the model pick a resolution automatically from the prompt. Valid range: 512*512–2048*2048.",
            "type": "string"
          },
          "n": {
            "description": "Number of images to generate.",
            "type": "integer",
            "default": 1,
            "minimum": 1,
            "maximum": 4
          },
          "negative_prompt": {
            "description": "Describes content that should NOT appear in the generated image.",
            "type": "string"
          },
          "seed": {
            "description": "Seed for reproducible generation, range [0, 2147483647]. Omit for a random seed.",
            "type": "integer",
            "minimum": 0,
            "maximum": 2147483647
          },
          "prompt_extend": {
            "description": "Enable intelligent prompt rewriting for richer results.",
            "type": "boolean",
            "default": true
          },
          "prompt_extend_mode": {
            "description": "Prompt rewriting strategy. 'direct' (DPE) rewrites in a single pass; 'agent' (APE) uses an agentic rewrite pipeline.",
            "type": "string",
            "default": "direct",
            "enum": [
              "direct",
              "agent"
            ],
            "x-ui-component": "select"
          }
        },
        "required": [
          "model",
          "prompt"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "prompt",
          "size",
          "n",
          "negative_prompt",
          "seed",
          "prompt_extend",
          "prompt_extend_mode"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "qwen-image-3.0/text-to-image"
          },
          "created_at": {
            "description": "ISO timestamp of when the request was created (e.g., \"2023-04-01T12:34:56.789Z\").",
            "format": "date-time",
            "type": "string"
          },
          "has_nsfw_contents": {
            "description": "Array of boolean values indicating NSFW detection for each output.",
            "items": {
              "type": "boolean"
            },
            "type": "array"
          },
          "id": {
            "description": "Unique identifier for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the generated images. Null when status is not completed. URLs are valid for about 24 hours — download promptly.",
            "items": {
              "type": "string"
            },
            "nullable": true,
            "type": "array"
          },
          "status": {
            "description": "Status of the task: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          }
        },
        "type": "object"
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  }
}

LLM-Friendly Prompt Template

# qwen-image-3.0/text-to-image

> Generates images from a text prompt at resolutions up to 2048×2048, with automatic prompt rewriting and prompt-guided resolution selection, building on Qwen strength in complex text rendering and precise prompt adherence


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `qwen-image-3.0/text-to-image`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"qwen-image-3.0/text-to-image"`

- **`prompt`** (`string`, _required_):
  The text prompt describing the image to generate.

- **`size`** (`string`, _optional_):
  Output resolution as width*height (e.g. 2048*2048). Omit to let the model pick a resolution automatically from the prompt. Valid range: 512*512–2048*2048.

- **`n`** (`integer`, _optional_):
  Number of images to generate.
  - Default: `1`
  - Min: 1
  - Max: 4

- **`negative_prompt`** (`string`, _optional_):
  Describes content that should NOT appear in the generated image.

- **`seed`** (`integer`, _optional_):
  Seed for reproducible generation, range [0, 2147483647]. Omit for a random seed.
  - Min: 0
  - Max: 2147483647

- **`prompt_extend`** (`boolean`, _optional_):
  Enable intelligent prompt rewriting for richer results.
  - Default: `true`

- **`prompt_extend_mode`** (`string`, _optional_):
  Prompt rewriting strategy. 'direct' (DPE) rewrites in a single pass; 'agent' (APE) uses an agentic rewrite pipeline.
  - Default: `"direct"`
  - Options: "direct", "agent"



**Required Parameters Example**:

```json
{
  "model": "qwen-image-3.0/text-to-image",
  "prompt": ""
}
```


**Full Example**:

```json
{
  "model": "qwen-image-3.0/text-to-image",
  "prompt": "",
  "size": "",
  "n": 1,
  "negative_prompt": "",
  "seed": 0,
  "prompt_extend": true,
  "prompt_extend_mode": "direct"
}
```


### Output Schema

The API returns the following output format:


- **`model`** (`string`, _optional_):
  model name
  - Default: `"qwen-image-3.0/text-to-image"`

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

- **`has_nsfw_contents`** (`array[boolean]`, _optional_):
  Array of boolean values indicating NSFW detection for each output.

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated images. Null when status is not completed. URLs are valid for about 24 hours — download promptly.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example Response**:

```json
{
  "model": "qwen-image-3.0/text-to-image",
  "created_at": "",
  "has_nsfw_contents": [],
  "id": "",
  "outputs": [
    ""
  ],
  "status": "",
  "urls": {}
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "qwen-image-3.0/text-to-image",
  "prompt": "",
  "size": "",
  "n": 1,
  "negative_prompt": "",
  "seed": 0,
  "prompt_extend": true,
  "prompt_extend_mode": "direct"
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/qwen-image-3.0/text-to-image)

A group of close friends celebrating on a rooftop overlooking a futuristic city, colorful fireworks illuminating the night sky, champagne glasses raised, laughter frozen in time, cinematic lighting, emotional storytelling, shallow depth of field, anamorphic lens, ultra realistic, 8K

Qwen-Image 3.0 (Text-to-Image)

Qwen-Image 3.0 is a high-fidelity text-to-image model that generates images from detailed text prompts. Built-in prompt expansion (DPE/APE) enriches short prompts, while flexible resolution and reproducible seeds make it suitable for both rapid exploration and controlled iteration.

Invite-beta model for interface testing. Output does not represent final quality.

🌟 Key Features

✏️ Text-to-Image

Generate high-fidelity images from text prompts alone, with strong semantic understanding and photorealistic quality.

🧠 Prompt Expansion (DPE / APE)

prompt_extend intelligently rewrites short prompts. Choose direct (DPE, single-pass) or agent (APE, agentic pipeline).

📐 Flexible Resolution

Specify size as width*height (up to 2048×2048), or omit it to let the model pick automatically from the prompt.

🔁 Reproducible Results

Pin a seed value to reproduce or iterate on a specific generation, enabling systematic prompt refinement.

🖼 Batch Generation

Request multiple images per call with n to explore variations at once.

⚙️ Parameters

Parameter	Required	Description
`prompt`	✅	Text description of the image to generate.
`size`	❌	Output resolution as `widthheight` (e.g. `20482048`). Omit for automatic. Range: 512×512–2048×2048.
`n`	❌	Number of images to generate (default: 1).
`negative_prompt`	❌	Content that should NOT appear in the image.
`seed`	❌	Seed for reproducible output, range `[0, 2147483647]`. Omit for random.
`prompt_extend`	❌	Enable intelligent prompt rewriting (default: `true`).
`prompt_extend_mode`	❌	`direct` (DPE, default) or `agent` (APE).

💡 Prompt Tips

A vertical outdoor portrait in warm afternoon light: a young woman turns to the camera with a bright smile, holding a large bouquet of orange and peach roses, backlit hair rimmed with gold, a blurred sunlit city street behind her, warm film grain.

Be specific about subject, environment, lighting, and camera angle for best results.
Fix seed when iterating on a prompt to isolate the effect of each change.
Keep prompt_extend on for short prompts; turn it off when you want the model to follow your exact wording.
Request multiple images with n to explore variations in a single call.

🎯 Use Cases

Creative Illustration — Generate concept art, characters, and environments from descriptive text prompts.
Marketing Visuals — Produce eye-catching posters, banners, and ad creatives quickly.
Social Media Content — Generate scroll-stopping visuals in portrait or square formats for any platform.
Rapid Prototyping — Iterate on creative directions at scale before committing to production assets.
Design Exploration — Batch-generate variations to compare styles, compositions, and color palettes.

📝 Notes

prompt is the only required field (besides model).
The platform model name is qwen-image-3.0-text-to-image; requests are dispatched upstream as qwen-image-invite-beta-v1.
size uses a width*height format (asterisk); valid range is 512×512–2048×2048.
Task status values: created, processing, completed, failed.
Generated image URLs are returned in outputs once status is completed, and are valid for about 24 hours — download promptly.

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Reve 2.1 Text-to-Image

Reve 2.1 is a layout-first text-to-image model that turns natural-language prompts into sharp, production-ready images at native 4K, with best-in-class typography and high prompt adherence.

Youchuan V8.2 Remove Background

Youchuan automatically removes the background from an input image, returning one transparent-background result.

Youchuan V8.2 Style Transfer

Youchuan retexture changes the artistic style of an input image while preserving its composition, returning four restyled results.

Youchuan V8.2 Blend

Youchuan V8.2 blends two to five input images into four fused results, with an optional guiding prompt and native 2K HD.

Youchuan V8.2 Image-to-Image

Youchuan V8.2 re-imagines an input image guided by a text prompt, returning four variations. Supports native 2K HD, style reference, and aspect-ratio / stylize / chaos / weird controls.

Youchuan V8.2 Text-to-Image

Youchuan V8.2 generates four images from a text prompt, with optional native 2K HD, a style reference, and aspect-ratio / stylize / chaos / weird controls.

Seedream v5.0 Pro Edit

ByteDance flagship next-generation image editing model. Supports up to 10 reference images while preserving identity, lighting, and color tones for professional-quality modifications.

Seedream v5.0 Pro Text-to-Image

ByteDance flagship next-generation image generation model with stronger prompt adherence, refined typography, and photorealistic detail. Single-image output at 1.5K and 2K tiers with JPEG and PNG support.

Nano Banana 2 Lite Edit Developer

Google's fastest and most cost-efficient Nano Banana image model for editing, applying natural-language edits and multi-image composition to up to 14 reference images with low latency.

From$0.04/PIC

$0.028/PIC

-30%

One API for All Media AI.

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